Literature DB >> 24530536

Application of vascular bundle displacement in the optic disc for glaucoma detection using fundus images.

José Abel de la Fuente-Arriaga1, Edgardo M Felipe-Riverón2, Eduardo Garduño-Calderón3.   

Abstract

This paper presents a methodology for glaucoma detection based on measuring displacements of blood vessels within the optic disc (vascular bundle) in human retinal images. The method consists of segmenting the region of the vascular bundle in an optic disc to set a reference point in the temporal side of the cup, determining the position of the centroids of the superior, inferior, and nasal vascular bundle segmented zones located within the segmented region, and calculating the displacement from normal position using the chessboard distance metric. The method was successful in 62 images out of 67, achieving 93.02% sensitivity, 91.66% specificity, and 91.34% global accuracy in pre-diagnosis.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Chessboard distance metric; Excavation detection; Glaucoma detection; Optic papilla segmentation; Vascular bundle displacement

Mesh:

Year:  2014        PMID: 24530536     DOI: 10.1016/j.compbiomed.2014.01.005

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  5 in total

1.  Utilizing a responsive web portal for studying disc tracing agreement in retinal images.

Authors:  Abdullah Sarhan; Andrew Swift; Adam Gorner; Jon Rokne; Reda Alhajj; Gavin Docherty; Andrew Crichton
Journal:  PLoS One       Date:  2021-05-25       Impact factor: 3.240

2.  Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images.

Authors:  Muhammad Salman Haleem; Liangxiu Han; Jano van Hemert; Alan Fleming; Louis R Pasquale; Paolo S Silva; Brian J Song; Lloyd Paul Aiello
Journal:  J Med Syst       Date:  2016-04-16       Impact factor: 4.460

3.  Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning.

Authors:  Muhammad Naseer Bajwa; Muhammad Imran Malik; Shoaib Ahmed Siddiqui; Andreas Dengel; Faisal Shafait; Wolfgang Neumeier; Sheraz Ahmed
Journal:  BMC Med Inform Decis Mak       Date:  2019-07-17       Impact factor: 2.796

4.  Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs?

Authors:  Sangeeta Biswas; Md Iqbal Aziz Khan; Md Tanvir Hossain; Angkan Biswas; Takayoshi Nakai; Johan Rohdin
Journal:  Life (Basel)       Date:  2022-06-28

5.  Statistical atlas-based descriptor for an early detection of optic disc abnormalities.

Authors:  Fantin Girard; Conrad Kavalec; Farida Cheriet
Journal:  J Med Imaging (Bellingham)       Date:  2018-03-06
  5 in total

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